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Record W4207073861 · doi:10.3390/rel13020108

Paying Attention: An Examination of Attention and Empathy as They Relate to Buddhist Philosophy

2022· article· en· W4207073861 on OpenAlexaff
Jennifer S. Carmichael

Bibliographic record

VenueReligions · 2022
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsYork University
Fundersnot available
KeywordsEmpathyBuddhismEmptinessEmbodied cognitionAestheticsPsychologyMindfulnessEpistemologySociologySocial psychologyPhilosophyPsychotherapist

Abstract

fetched live from OpenAlex

The human response to the COVID-19 pandemic has exposed a concerning decline in empathy for each other and the planet. A dualistic conception of mind and body coupled with a capitalist society that requires belief in an inherent self to fuel consumerism both complicate our ability to empathize because these ideas reify our conventional self. This paper argues that an understanding of the Buddhist conception of emptiness as explored in Nagarjuna’s Fundamental Wisdom of the Middle Way (Mūlamadhyamakakārikā) paired with mindful observation of embodied physical experience can allow for an understanding of “self” as a web of interacting processes within the larger web of interacting processes which constitutes the world. This can facilitate a shift in our mode of engagement with the world towards one of empathy because it demonstrates the emptiness of essence of an inherent self and instead situates the conventional “self” as interrelated with the world. Touching on related concepts such as Thich Nhat Hanh’s interbeing, this paper argues that contemplating emptiness while practicing Buddhist mindfulness techniques rooted in bodily sensation can facilitate empathy, which allows for the possibility of not only recovering from the COVID-19 pandemic, but also of rebuilding our global community and thriving as a more empathetic society in the future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.321
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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